Imporing Required Libraries

Value Counts

Filtering Data

Missing Values Treatment

Checking for any Duplicated Values

Exploratory Data Analysis

Hist and KDE(Kernal Density Estimate ) plots

Correlation

Feature Scalling

Finding Optimal Number of Clusters1

Identified cluster number is appling on each column for the scaled data

Finding the labels

To display the centroids1

Centroids represented in the dataframe

Performing Inverese Transform to get datapoints in orginal form

concatenate the clusters labels to our original dataframe

Dimensional Reduction

Principle Component Analysis